Future wireless communication systems are expected to provide a concurrent connection of multiple data streams to the users. A massive Multiple Input Multiple Output (mMIMO) access node (e.g. a network node, such as a base station (BS)) can be used to provide multiple streams of data to a single user (such as a User Equipment (UE)), which is equipped with multiple antennas. To enable mMIMO communications, the spatial radio channels should be acquired. To learn the radio channels in the timefrequency grid in the Time Division Duplex (TDD) mode for mMIMO communication with a user with nr antennas, nr orthogonal pilot symbols, each associated to an antenna of the user, are required over a timefrequency grid of the size Tc×Bc regardless of number of antennas of the access node, where Tc is the coherence time of the channel and Bc is the coherence bandwidth of the channel. This is a reason that TDD is an appealing solution for mMIMO. However, the conventional TDD solution cannot be applied for Frequency Division Duplex (FDD) downlink transmission as crystallized by the following three issues.
Conventional long term evolution (LTE) solution for FDD does not work for FDD mMIMO where for example the massive antenna array contains hundreds of antennas. To illustrate this, for simplicity we assume that channel between the transmitter and receiver are unknown constants. To learn the channel (e.g., the equivalent complex number affecting the narrowband transmitted signals), at least one linear equation per number of unknowns is needed to find a meaningful estimation of the channel in general, and in particular when the antenna spacing is configured such that it results in a full rank channel matrix. So to learn for example a downlink channel from a base station with nt antennas to a user each with nr antennas, at least nt pilot signals are needed; i.e. one pilot per antenna, or alternatively nt orthogonal sequences of length nt (or spanning a subspace with dimension nt) are required. For uplink transmission, however the required number of pilot symbols changes to nr.
The density of pilot symbols depends on the radio channel characteristic which changes over time and frequency. However, the variations in time depend on the mobility of the users (e.g. mobile users). The faster the users move, the faster the channel in time changes due to a larger Doppler frequency. The radio channel can be assumed unchanged within the coherence time Tc, which is a function of the carrier frequency and the velocity of the user. So to learn the channel between transmit and receive antenna ports over a coherence time, at least one pilot symbol per coherence time is needed. Similarly, the radio channel varies in frequency. However, the changes in the frequency are generally characterized by the coherence bandwidth, Bc which depends on the delay profile of the channel and the symbol duration. So via the conventional pilot transmission, one can see that the number of pilot symbols increases linearly with the number of antennas and hence it does not scale favorably for massive antenna arrays.
Assume hypothetically that the users have found the channels. Then there will be nt coefficients per antenna port at the user, which are needed to be fed back to the base station. The conventional feedback of these coefficients results in a high overhead and is not scalable with the number of transmit antennas.
Having learned the channels and transmitted feedback, then it is essential to find precoding strategies that enable concurrent multi-stream transmission over shared time frequency resources. Finding the precoder is tightly connected to the spatial channel estimation.
In a conventional method for multi-stream downlink transmission for FDD MIMO links the transmitter is configured to coordinate transmission of pilot sequences in a coherence interval over a subset of antenna ports to the user; and to receive precoding matrix index (PMI) and rank indication (RI) via which to further configure the transmission of the plurality of jointly spatially precoded symbol sequences of the said users. In LTE and LTE Advanced, this precoding strategy is standardized. However, this solution requires channel training over each antenna port and the conventional channel training cannot be extended to mMIMO due to very high pilot and feedback transmission overhead.
To reduce uplink overhead for 8 Tx MIMO, LTE includes a double codebook structure, i.e. a cascade precoder, targeting closely spaced antennas implying spatial correlation. The first feedback link tracks long-term/wideband channel fluctuation while the second feedback channel carries short-term/sub-band channel state information (CSI). To reduce the feedback in mMIMO, there is also a similar two-stage precoding where one stage is updated less often and hence requires less feedback.
Nevertheless, this solution also suffers from the overhead in pilot transmission to learn the channels at the user side. The conventional solutions further require that the transmitter send a large amount of pilot symbols from the mMIMO base stations which scales linearly with number of antennas using the classical solutions as those practiced in LTE systems. Further, the conventional solutions require that the user searches for an appropriate precoding matrix. Since precoding codebook for large antennas need to be large enough to be enabling the gain of mMIMO arrays. This increases the battery consumption and complexity of the user for mMIMO systems. Under assumption that the user performs the correct channel estimation, it requires the feedback which consumes radio time-frequency resource, which could be otherwise used for UL data transmission for higher performance.